Vehicle Recommendation System Using Tendency Matching
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Solution Overview
Problem
Customers unfamiliar with cars face difficulties in using vehicle recommendation services, as existing systems pose complex questions and provide low satisfaction with their recommendations.
Innovation Solution
A vehicle recommendation system that includes an optimal tendency matcher to determine suitable vehicle types based on user tendencies, an option group classifier to group vehicle specifications, and an option group matcher to select optimal vehicles by calculating standard deviations and matching user tendencies with vehicle characteristics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If specialized vehicle questions are used in recommendation services, then the system can gather detailed vehicle preference information, but customers find it difficult to understand and use
Solution Approach 1:
The patent transforms specialized vehicle parameters into general lifestyle parameters. Instead of asking about specific vehicle specifications, the system asks about daily usage scenarios (commuting distance, family size, leisure activities). This parameter transformation maintains information gathering effectiveness while improving customer comprehension and ease of use.
Solution Approach 2:
The patent introduces an intermediary translation layer between customer responses and vehicle recommendations. The system uses natural language processing and machine learning models to interpret general customer preferences and translate them into appropriate vehicle specifications, bridging the gap between simple customer inputs and complex vehicle selection criteria.
2Measurement precision
If traditional recommendation systems ask detailed specialized questions, then they can potentially provide accurate recommendations, but customer satisfaction with the recommendation process and results is low
Solution Approach 1:
The patent inverts the traditional recommendation approach by not directly asking customers about vehicle preferences. Instead, it observes customer lifestyle patterns and infers vehicle preferences indirectly. This inversion maintains recommendation accuracy while significantly improving customer satisfaction, as users feel understood rather than interrogated.
Solution Approach 2:
The system implements continuous feedback loops where customer interactions, preferences, and satisfaction levels are continuously monitored and used to refine recommendation algorithms. This ensures both high recommendation accuracy and ongoing customer satisfaction by adapting to individual user needs and preferences over time.
Data Source
AI summary
A vehicle recommendation system includes: an optimum tendency matcher configured to determine an optimal vehicle type suitable for a user tendency among a plurality of vehicles based on the user tendency and a vehicle tendency of each of the plurality of vehicles; an option group classifier configured to generate a plurality of option groups by grouping predetermined option specifications, for each of a plurality of vehicle types, among a plurality of specifications of the vehicle type; and an option group matcher configured to determine an optimal vehicle by matching an option group corresponding to the user tendency among the plurality of option groups of the optimal vehicle type based on the user tendency.